Computer Vision

Researchers

Awards

AWARD Best Paper - Honorable Mention Award at WACV 2021
Date: January 6, 2021
Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
MERL Contact: Suhas Lohit
Research Areas: Computational Sensing, Computer Vision, Machine Learning
Brief
- A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".
  
  The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
AWARD MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision
Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim K. Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief
- MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
AWARD CVPR 2011 Longuet-Higgins Prize
Date: June 25, 2011
Awarded to: Paul A. Viola and Michael J. Jones
Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
MERL Contact: Michael J. Jones
Research Area: Machine Learning
Brief
- Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).

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News & Events

NEWS MERL contributes to ICRA 2025
Date: May 19, 2025 - May 23, 2025
Where: IEEE ICRA
MERL Contacts: Stefano Di Cairano; Jianlin Guo; Chiori Hori; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Philip V. Orlik; Arvind Raghunathan; Diego Romeres; Yuki Shirai; Abraham P. Vinod; Yebin Wang
Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, Robotics, Human-Computer Interaction
Brief
- MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2025, which was held in Atlanta, Georgia, USA, from May 19th to May 23rd.
  
  MERL was a Bronze sponsor of the conference, and MERL researchers chaired four sessions in the areas of Manipulation Planning, Human-Robot Collaboration, Diffusion Policy, and Learning for Robot Control.
  
  MERL researchers presented four papers in the main conference on the topics of contact-implicit trajectory optimization, proactive robotic assistance in human-robot collaboration, diffusion policy with human preferences, and dynamic and model learning of robotic manipulators. In addition, five more papers were presented in the workshops: “Structured Learning for Efficient, Reliable, and Transparent Robots,” “Safely Leveraging Vision-Language Foundation Models in Robotics: Challenges and Opportunities,” “Long-term Human Motion Prediction,” and “The Future of Intelligent Manufacturing: From Innovation to Implementation.”
  
  MERL researcher Diego Romeres delivered an invited talk titled “Dexterous Robotics: From Multimodal Sensing to Real-World Physical Interactions.”
  
  MERL also collaborated with the University of Padua on one of the conference’s challenges: the “3rd AI Olympics with RealAIGym” (https://ai-olympics.dfki-bremen.de).
  
  During the conference, MERL researchers received the IEEE Transactions on Automation Science and Engineering Best New Application Paper Award for their paper titled “Smart Actuation for End-Edge Industrial Control Systems.”
  
  About ICRA
  
  The IEEE International Conference on Robotics and Automation (ICRA) is the flagship conference of the IEEE Robotics and Automation Society and the world’s largest and most comprehensive technical conference focused on research advances and the latest technological developments in robotics. The event attracts over 7,000 participants, 143 partners and exhibitors, and receives more than 4,000 paper submissions.
NEWS Suhas Lohit presents invited talk at Boston Symmetry Day 2025
Date: March 31, 2025
Where: Northeastern University, Boston, MA
MERL Contact: Suhas Lohit
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief
- MERL researcher Suhas Lohit was an invited speaker at Boston Symmetry Day, held at Northeastern University. Boston Symmetry Day, an annual workshop organized by researchers at MIT and Northeastern, brought together attendees interested in symmetry-informed machine learning and its applications. Suhas' talk, titled “Efficiency for Equivariance, and Efficiency through Equivariance” discussed recent MERL works that show how to build general and efficient equivariant neural networks, and how equivariance can be utilized in self-supervised learning to yield improved 3D object detection. The abstract and slides can be found in the link below.

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Research Highlights

Internships

CV0063: Internship - Visual Simultaneous Localization and Mapping

MERL is looking for a self-motivated graduate student to work on Visual Simultaneous Localization and Mapping (V-SLAM). Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to): camera pose estimation, feature detection and matching, visual-LiDAR data fusion, pose-graph optimization, loop closure detection, and image-based camera relocalization. The ideal candidate would be a PhD student with a strong background in 3D computer vision and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.

Required Specific Experience
- Experience with 3D Computer Vision and Simultaneous Localization & Mapping.
ST0096: Internship - Multimodal Tracking and Imaging

MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.
Required Specific Experience
- Experience with Python and Python Deep Learning Frameworks.
- Experience with FMCW radar and/or Depth Sensors.
CA0153: Internship - High-Fidelity Visualization and Simulation for Space Applications

MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
Required Specific Experience
- Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field
- Experience with one or more of Blender, Unreal, Unity, along with their APIs
- Strong programming skills in one or more of Matlab, Python, and/or C/C++

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Openings

CI0130: Postdoctoral Research Fellow - Artificial General Intelligence (AGI)

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Recent Publications

Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Multimodal 3D Object Detection on Unseen Domains", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
BibTeX TR2025-078 PDF
- @inproceedings{Hegde2025jun,
- author = {Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.},
- title = {{Multimodal 3D Object Detection on Unseen Domains}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-078}
- }
Jung, Y.G., Park, J., Yoon, J., Peng, K.-C., Kim, W., Teoh, A.B.J., Camps, O., "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2025.
BibTeX TR2025-077 PDF Presentation
- @inproceedings{Jung2025jun,
- author = {{{Jung, Yoon G. and Park, Jaewoo and Yoon, Jaeho and Peng, Kuan-Chuan and Kim, Wonchul and Teoh, Andrew B. J. and Camps, Octavia}}},
- title = {{{TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection}}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-077}
- }
Lai, Y.-H., Ebbers, J., Wang, Y.-C.F., Germain, F.G., Jones, M.J., Chatterjee, M., "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2025.
BibTeX TR2025-072 PDF
- @inproceedings{Lai2025jun,
- author = {Lai, Yung-Hsuan and Ebbers, Janek and Wang, Yu-Chiang Frank and Germain, François G and Jones, Michael J. and Chatterjee, Moitreya},
- title = {{UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-072}
- }
Li, K., Zhang, T., Peng, K.-C., Wang, G., "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
BibTeX TR2025-076 PDF
- @inproceedings{Li2025jun,
- author = {Li, Kaidong and Zhang, Tianxiao and Peng, Kuan-Chuan and Wang, Guanghui},
- title = {{PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-076}
- }
Ni, Y., Wen, S., Koniusz, P., Cherian, A., "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), June 2025.
BibTeX TR2025-073 PDF
- @inproceedings{Ni2025jun,
- author = {Ni, Yao and Wen, Song and Koniusz, Piotr and Cherian, Anoop},
- title = {{Noise Consistency Regularization for Improved Subject-Driven Image Synthesis}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-073}
- }
Sawada, N., Miraldo, P., Lohit, S., Marks, T.K., Chatterjee, M., "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations", IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR), June 2025.
BibTeX TR2025-074 PDF
- @inproceedings{Sawada2025jun,
- author = {Sawada, Naoko and Miraldo, Pedro and Lohit, Suhas and Marks, Tim K. and Chatterjee, Moitreya},
- title = {{FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-074}
- }
Lu, K., Ma, C., Hori, C., Romeres, D., "KitchenVLA: Iterative Vision-Language Corrections for Robotic Execution of Human Tasks", IEEE International Conference on Robotics and Automation Workshop on Safely Leveraging Vision-Language Foundation Models in Robotics (SafeLVMs@ICRA), May 2025.
BibTeX TR2025-068 PDF
- @inproceedings{Lu2025may,
- author = {Lu, Kai and Ma, Chenyang and Hori, Chiori and Romeres, Diego},
- title = {{KitchenVLA: Iterative Vision-Language Corrections for Robotic Execution of Human Tasks}},
- booktitle = {IEEE International Conference on Robotics and Automation Workshop on Safely Leveraging Vision-Language Foundation Models in Robotics (SafeLVMs@ICRA)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-068}
- }
Singh, A., Jones, M.J., Peng, K.-C., Chatterjee, M., Cherian, A., Learned-Miller, E., "Improving Open-World Object Localization by Discovering Background", CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon, May 2025.
BibTeX TR2025-058 PDF
- @inproceedings{Singh2025may,
- author = {Singh, Ashish and Jones, Michael J. and Peng, Kuan-Chuan and Chatterjee, Moitreya and Cherian, Anoop and Learned-Miller, Erik},
- title = {{Improving Open-World Object Localization by Discovering Background}},
- booktitle = {CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-058}
- }